Skill UI

This skill performs security-focused code reviews for pull requests, commits, and diffs. It prioritizes high-risk areas like authentication, cryptography, and external calls. The process is evidence-based, requiring specific line numbers and attack scenarios. It adapts to codebase size and always generates a comprehensive markdown report.

This skill creates comprehensive technical documentation from existing codebases. It analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks. The documentation includes executive summaries, architecture overviews, design decisions, core components, data models, and deployment considerations. It helps onboarding new team members and supports long-term maintenance by providing clear, structured, and accessible technical references in Markdown format.

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

Maintain durable feature-level memory across AI coding sessions using lightweight Markdown tracks. It organizes status, source-of-truth docs, decisions, risks, and changes for long-lived features. Helps AI agents resume work accurately without relying on chat history. Ideal for repositories needing structured project memory without reorganizing all documentation.

Generate professional PDF reports from GEO audit markdown files using pandoc and headless Chrome. Extracts metadata like brand name, domain, GEO score, and creates color-coded score tables with severity-tagged finding sections. Ideal for client deliverables in SEO and Generative Engine Optimization projects. Requires pandoc and Google Chrome installed.

This skill creates or updates a shared Apple design context document for other HIG skills. It auto-discovers project details from code and assets, then gathers information on product overview, platforms, technology stack, design system, accessibility, and user context. The output is a structured markdown file used to tailor subsequent design guidance.

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles. Ideal for AI engineers and researchers.

Look up Hugging Face paper pages as markdown and query the papers API for structured metadata including authors, linked models, datasets, Spaces, GitHub repos, and project pages. Ideal for summarizing or analyzing AI research papers from hf.co or arXiv URLs and IDs.

Convert Hugo documentation sites and Hugo-managed content into standard Markdown by inspecting local Hugo config, shortcodes, render hooks, and content conventions. Useful for migrating, archiving, or integrating Hugo docs into Markdown workflows.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

This skill extracts full transcripts and rich metadata from a single YouTube video URL. It uses yt-dlp to download subtitles, cleans VTT timing markers, and pulls video details (title, channel, date). The output is a structured, queryable Markdown file, ideal for populating a personal knowledge vault and enabling downstream knowledge graph extraction or content repurposing.

Framework-agnostic long-term project memory for AI agents. Persists architecture decisions, conventions, and context as plain Markdown files in .lore/, enabling any agent to record, recall, audit, sync, and query project knowledge.